Broström, Elin

KTH, School of Electrical Engineering (EES), Electric Power Systems.

2007 (English)Licentiate thesis, monograph (Other scientific)

Abstract [en]

In this thesis a new technique of modelling non-dimensioning severe weather for power system reliability calculations is developed. The model is suitable for both transmission and distribution networks and is based on geographically moving winds and ice storms. The modelled weather has severity levels that vary with time and change continuously as the weather passes a region. Different weather situations are represented with scenarios. For each scenario the weather parameters, such as size, strength, speed and direction can vary. A stochastic method for choosing parameters is also described. This method is based on probabilities for different weather situations for Swedish conditions. A stochastic vulnerability model for the components is required for each scenario to connect the risk of failure to the weather situation. The model developed here connects the direct wind impact with the impact from the ice storm which is given by an ice accretion model. It is assumed that the probability for an individual segment to break down due to the impact of a given weather depends on load functions for wind and ice together with the vulnerability model for components. It is possible to estimate the outage risk as well as the time difference between mean times to failure in different lines. Monte Carlo methods, where many scenarios are simulated, are used in the case studies. Studies of the system vulnerability is a future work of this project but in one small case study the probability for outage in a load point is estimated. To be able to estimate repair times after a severe weather the reliability calculations are extended with a restoration model which gives distributions of down times for the broken components. The situations after the ice storm that are studied are so severe that gathering of all or almost all possible restoration resources is required to restore the system. Restoration times for different components are not assumed to be independent; on the other hand they are assumed to be strongly correlated. The restoration process is dependent on staff situation, distance between location of spare parts and the breakdown, forecasts, availability of roads and distance to other breakdowns; this is included in the model. A method for simulation of non-Gaussian correlated random numbers is developed to include the correlations during the restoration process. The case studies show the impact of the different weather situations on the components and the following restoration times for the broken components.